Fecal microbiome-based clustering identifies distinct disease subtypes with differing metabolomic profiles and treatment outcomes in canine chronic enteropathy

Canine chronic enteropathy (CE) is a heterogeneous disorder with variable clinical outcomes. We aimed to identify fecal microbiome and metabolomic signatures associated with CE severity and treatment outcomes. Fecal samples from dogs with CE (n = 56), acute diarrhea (n = 8), and healthy controls (n = 126) were analyzed by 16S rRNA gene sequencing and GC-MS-based untargeted metabolomics. CE dogs exhibited significantly reduced microbial diversity compared with the other groups, and unsupervised Partitioning Around Medoids clustering identified two microbiome clusters within the CE cohort. Cluster 2 was characterized by dysbiosis, with enrichment of Streptococcus, Enterococcus, and Klebsiella, and depletion of short-chain fatty acid producers, including Fusobacterium and Blautia. Among dogs with baseline Canine Chronic Enteropathy Clinical Activity Index (CCECAI) > 3, 10 of 12 Cluster 1 dogs (83.3%) achieved remission (CCECAI ≤ 3) after treatment, compared with 7 of 18 Cluster 2 dogs (38.9%; p = 0.0256). CCECAI scores decreased significantly after treatment in Cluster 1 (p = 0.0005) but not in Cluster 2 (p = 0.077). Fecal propanoic acid and kynurenic acid levels also differed between clusters. These findings suggest that gut microbiota and associated metabolites may contribute to differences in CE severity and treatment outcomes.